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.gitattributes

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.gitignore

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+.DS_Store
+debug*
+datasets/
+checkpoints/
+results/
+build/
+dist/
+*.png
+torch.egg-info/
+*/**/__pycache__
+torch/version.py
+torch/csrc/generic/TensorMethods.cpp
+torch/lib/*.so*
+torch/lib/*.dylib*
+torch/lib/*.h
+torch/lib/build
+torch/lib/tmp_install
+torch/lib/include
+torch/lib/torch_shm_manager
+torch/csrc/cudnn/cuDNN.cpp
+torch/csrc/nn/THNN.cwrap
+torch/csrc/nn/THNN.cpp
+torch/csrc/nn/THCUNN.cwrap
+torch/csrc/nn/THCUNN.cpp
+torch/csrc/nn/THNN_generic.cwrap
+torch/csrc/nn/THNN_generic.cpp
+torch/csrc/nn/THNN_generic.h
+docs/src/**/*
+test/data/legacy_modules.t7
+test/data/gpu_tensors.pt
+test/htmlcov
+test/.coverage
+*/*.pyc
+*/**/*.pyc
+*/**/**/*.pyc
+*/**/**/**/*.pyc
+*/**/**/**/**/*.pyc
+*/*.so*
+*/**/*.so*
+*/**/*.dylib*
+test/data/legacy_serialized.pt
+*~
+.idea
+
+#Ignore Wandb
+wandb/

.replit

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+language = "python3"
+run = "<p><a href=\"https://github.com/affinelayer/pix2pix-tensorflow\"> [Tensorflow]</a> (by Christopher Hesse), <a href=\"https://github.com/Eyyub/tensorflow-pix2pix\">[Tensorflow]</a> (by Eyyüb Sariu), <a href=\"https://github.com/datitran/face2face-demo\"> [Tensorflow (face2face)]</a> (by Dat Tran), <a href=\"https://github.com/awjuliani/Pix2Pix-Film\"> [Tensorflow (film)]</a> (by Arthur Juliani), <a href=\"https://github.com/kaonashi-tyc/zi2zi\">[Tensorflow (zi2zi)]</a> (by Yuchen Tian), <a href=\"https://github.com/pfnet-research/chainer-pix2pix\">[Chainer]</a> (by mattya), <a href=\"https://github.com/tjwei/GANotebooks\">[tf/torch/keras/lasagne]</a> (by tjwei), <a href=\"https://github.com/taey16/pix2pixBEGAN.pytorch\">[Pytorch]</a> (by taey16) </p> </ul>"

CycleGAN.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/bkkaggle/pytorch-CycleGAN-and-pix2pix/blob/master/CycleGAN.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "5VIGyIus8Vr7"
+   },
+   "source": [
+    "Take a look at the [repository](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) for more information"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "7wNjDKdQy35h"
+   },
+   "source": [
+    "# Install"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "TRm-USlsHgEV"
+   },
+   "outputs": [],
+   "source": [
+    "!git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "Pt3igws3eiVp"
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "os.chdir('pytorch-CycleGAN-and-pix2pix/')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "z1EySlOXwwoa"
+   },
+   "outputs": [],
+   "source": [
+    "!pip install -r requirements.txt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "8daqlgVhw29P"
+   },
+   "source": [
+    "# Datasets\n",
+    "\n",
+    "Download one of the official datasets with:\n",
+    "\n",
+    "-   `bash ./datasets/download_cyclegan_dataset.sh [apple2orange, summer2winter_yosemite, horse2zebra, monet2photo, cezanne2photo, ukiyoe2photo, vangogh2photo, maps, cityscapes, facades, iphone2dslr_flower, ae_photos]`\n",
+    "\n",
+    "Or use your own dataset by creating the appropriate folders and adding in the images.\n",
+    "\n",
+    "-   Create a dataset folder under `/dataset` for your dataset.\n",
+    "-   Create subfolders `testA`, `testB`, `trainA`, and `trainB` under your dataset's folder. Place any images you want to transform from a to b (cat2dog) in the `testA` folder, images you want to transform from b to a (dog2cat) in the `testB` folder, and do the same for the `trainA` and `trainB` folders."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "vrdOettJxaCc"
+   },
+   "outputs": [],
+   "source": [
+    "!bash ./datasets/download_cyclegan_dataset.sh horse2zebra"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "gdUz4116xhpm"
+   },
+   "source": [
+    "# Pretrained models\n",
+    "\n",
+    "Download one of the official pretrained models with:\n",
+    "\n",
+    "-   `bash ./scripts/download_cyclegan_model.sh [apple2orange, orange2apple, summer2winter_yosemite, winter2summer_yosemite, horse2zebra, zebra2horse, monet2photo, style_monet, style_cezanne, style_ukiyoe, style_vangogh, sat2map, map2sat, cityscapes_photo2label, cityscapes_label2photo, facades_photo2label, facades_label2photo, iphone2dslr_flower]`\n",
+    "\n",
+    "Or add your own pretrained model to `./checkpoints/{NAME}_pretrained/latest_net_G.pt`"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "B75UqtKhxznS"
+   },
+   "outputs": [],
+   "source": [
+    "!bash ./scripts/download_cyclegan_model.sh horse2zebra"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "yFw1kDQBx3LN"
+   },
+   "source": [
+    "# Training\n",
+    "\n",
+    "-   `python train.py --dataroot ./datasets/horse2zebra --name horse2zebra --model cycle_gan`\n",
+    "\n",
+    "Change the `--dataroot` and `--name` to your own dataset's path and model's name. Use `--gpu_ids 0,1,..` to train on multiple GPUs and `--batch_size` to change the batch size. I've found that a batch size of 16 fits onto 4 V100s and can finish training an epoch in ~90s.\n",
+    "\n",
+    "Once your model has trained, copy over the last checkpoint to a format that the testing model can automatically detect:\n",
+    "\n",
+    "Use `cp ./checkpoints/horse2zebra/latest_net_G_A.pth ./checkpoints/horse2zebra/latest_net_G.pth` if you want to transform images from class A to class B and `cp ./checkpoints/horse2zebra/latest_net_G_B.pth ./checkpoints/horse2zebra/latest_net_G.pth` if you want to transform images from class B to class A.\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0sp7TCT2x9dB"
+   },
+   "outputs": [],
+   "source": [
+    "!python train.py --dataroot ./datasets/horse2zebra --name horse2zebra --model cycle_gan --display_id -1"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "9UkcaFZiyASl"
+   },
+   "source": [
+    "# Testing\n",
+    "\n",
+    "-   `python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout`\n",
+    "\n",
+    "Change the `--dataroot` and `--name` to be consistent with your trained model's configuration.\n",
+    "\n",
+    "> from https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix:\n",
+    "> The option --model test is used for generating results of CycleGAN only for one side. This option will automatically set --dataset_mode single, which only loads the images from one set. On the contrary, using --model cycle_gan requires loading and generating results in both directions, which is sometimes unnecessary. The results will be saved at ./results/. Use --results_dir {directory_path_to_save_result} to specify the results directory.\n",
+    "\n",
+    "> For your own experiments, you might want to specify --netG, --norm, --no_dropout to match the generator architecture of the trained model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "uCsKkEq0yGh0"
+   },
+   "outputs": [],
+   "source": [
+    "!python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "OzSKIPUByfiN"
+   },
+   "source": [
+    "# Visualize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "9Mgg8raPyizq"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_fake.png')\n",
+    "plt.imshow(img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "colab": {},
+    "colab_type": "code",
+    "id": "0G3oVH9DyqLQ"
+   },
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "img = plt.imread('./results/horse2zebra_pretrained/test_latest/images/n02381460_1010_real.png')\n",
+    "plt.imshow(img)"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "name": "CycleGAN",
+   "provenance": []
+  },
+  "environment": {
+   "name": "tf2-gpu.2-3.m74",
+   "type": "gcloud",
+   "uri": "gcr.io/deeplearning-platform-release/tf2-gpu.2-3:m74"
+  },
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.7.10"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}

LICENSE

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+Copyright (c) 2017, Jun-Yan Zhu and Taesung Park
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+
+
+--------------------------- LICENSE FOR pix2pix --------------------------------
+BSD License
+
+For pix2pix software
+Copyright (c) 2016, Phillip Isola and Jun-Yan Zhu
+All rights reserved.
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+* Redistributions of source code must retain the above copyright notice, this
+  list of conditions and the following disclaimer.
+
+* Redistributions in binary form must reproduce the above copyright notice,
+  this list of conditions and the following disclaimer in the documentation
+  and/or other materials provided with the distribution.
+
+----------------------------- LICENSE FOR DCGAN --------------------------------
+BSD License
+
+For dcgan.torch software
+
+Copyright (c) 2015, Facebook, Inc. All rights reserved.
+
+Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met:
+
+Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer.
+
+Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution.
+
+Neither the name Facebook nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission.
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.

README.md

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+
+<img src='imgs/horse2zebra.gif' align="right" width=384>
+
+<br><br><br>
+
+# CycleGAN and pix2pix in PyTorch
+
+**New**:  Please check out [contrastive-unpaired-translation](https://github.com/taesungp/contrastive-unpaired-translation) (CUT), our new unpaired image-to-image translation model that enables fast and memory-efficient training.
+
+We provide PyTorch implementations for both unpaired and paired image-to-image translation.
+
+The code was written by [Jun-Yan Zhu](https://github.com/junyanz) and [Taesung Park](https://github.com/taesungp), and supported by [Tongzhou Wang](https://github.com/SsnL).
+
+This PyTorch implementation produces results comparable to or better than our original Torch software. If you would like to reproduce the same results as in the papers, check out the original [CycleGAN Torch](https://github.com/junyanz/CycleGAN) and [pix2pix Torch](https://github.com/phillipi/pix2pix) code in Lua/Torch.
+
+**Note**: The current software works well with PyTorch 1.4. Check out the older [branch](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/tree/pytorch0.3.1) that supports PyTorch 0.1-0.3.
+
+You may find useful information in [training/test tips](docs/tips.md) and [frequently asked questions](docs/qa.md). To implement custom models and datasets, check out our [templates](#custom-model-and-dataset). To help users better understand and adapt our codebase, we provide an [overview](docs/overview.md) of the code structure of this repository.
+
+**CycleGAN: [Project](https://junyanz.github.io/CycleGAN/) |  [Paper](https://arxiv.org/pdf/1703.10593.pdf) |  [Torch](https://github.com/junyanz/CycleGAN) |
+[Tensorflow Core Tutorial](https://www.tensorflow.org/tutorials/generative/cyclegan) | [PyTorch Colab](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/CycleGAN.ipynb)**
+
+<img src="https://junyanz.github.io/CycleGAN/images/teaser_high_res.jpg" width="800"/>
+
+**Pix2pix:  [Project](https://phillipi.github.io/pix2pix/) |  [Paper](https://arxiv.org/pdf/1611.07004.pdf) |  [Torch](https://github.com/phillipi/pix2pix) |
+[Tensorflow Core Tutorial](https://www.tensorflow.org/tutorials/generative/pix2pix) | [PyTorch Colab](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/pix2pix.ipynb)**
+
+<img src="https://phillipi.github.io/pix2pix/images/teaser_v3.png" width="800px"/>
+
+
+**[EdgesCats Demo](https://affinelayer.com/pixsrv/) | [pix2pix-tensorflow](https://github.com/affinelayer/pix2pix-tensorflow) | by [Christopher Hesse](https://twitter.com/christophrhesse)**
+
+<img src='imgs/edges2cats.jpg' width="400px"/>
+
+If you use this code for your research, please cite:
+
+Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks.<br>
+[Jun-Yan Zhu](https://www.cs.cmu.edu/~junyanz/)\*,  [Taesung Park](https://taesung.me/)\*, [Phillip Isola](https://people.eecs.berkeley.edu/~isola/), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros). In ICCV 2017. (* equal contributions) [[Bibtex]](https://junyanz.github.io/CycleGAN/CycleGAN.txt)
+
+
+Image-to-Image Translation with Conditional Adversarial Networks.<br>
+[Phillip Isola](https://people.eecs.berkeley.edu/~isola), [Jun-Yan Zhu](https://www.cs.cmu.edu/~junyanz/), [Tinghui Zhou](https://people.eecs.berkeley.edu/~tinghuiz), [Alexei A. Efros](https://people.eecs.berkeley.edu/~efros). In CVPR 2017. [[Bibtex]](https://www.cs.cmu.edu/~junyanz/projects/pix2pix/pix2pix.bib)
+
+## Talks and Course
+pix2pix slides: [keynote](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/pix2pix.key) | [pdf](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/pix2pix.pdf),
+CycleGAN slides: [pptx](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/CycleGAN.pptx) | [pdf](http://efrosgans.eecs.berkeley.edu/CVPR18_slides/CycleGAN.pdf)
+
+CycleGAN course assignment [code](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/assignments/a4-code.zip) and [handout](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/assignments/a4-handout.pdf) designed by Prof. [Roger Grosse](http://www.cs.toronto.edu/~rgrosse/) for [CSC321](http://www.cs.toronto.edu/~rgrosse/courses/csc321_2018/) "Intro to Neural Networks and Machine Learning" at University of Toronto. Please contact the instructor if you would like to adopt it in your course.
+
+## Colab Notebook
+TensorFlow Core CycleGAN Tutorial: [Google Colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/cyclegan.ipynb) | [Code](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/cyclegan.ipynb)
+
+TensorFlow Core pix2pix Tutorial: [Google Colab](https://colab.research.google.com/github/tensorflow/docs/blob/master/site/en/tutorials/generative/pix2pix.ipynb) | [Code](https://github.com/tensorflow/docs/blob/master/site/en/tutorials/generative/pix2pix.ipynb)
+
+PyTorch Colab notebook: [CycleGAN](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/CycleGAN.ipynb) and [pix2pix](https://colab.research.google.com/github/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/pix2pix.ipynb)
+
+ZeroCostDL4Mic Colab notebook: [CycleGAN](https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks_Beta/CycleGAN_ZeroCostDL4Mic.ipynb) and [pix2pix](https://colab.research.google.com/github/HenriquesLab/ZeroCostDL4Mic/blob/master/Colab_notebooks_Beta/pix2pix_ZeroCostDL4Mic.ipynb)
+
+## Other implementations
+### CycleGAN
+<p><a href="https://github.com/leehomyc/cyclegan-1"> [Tensorflow]</a> (by Harry Yang),
+<a href="https://github.com/architrathore/CycleGAN/">[Tensorflow]</a> (by Archit Rathore),
+<a href="https://github.com/vanhuyz/CycleGAN-TensorFlow">[Tensorflow]</a> (by Van Huy),
+<a href="https://github.com/XHUJOY/CycleGAN-tensorflow">[Tensorflow]</a> (by Xiaowei Hu),
+<a href="https://github.com/LynnHo/CycleGAN-Tensorflow-2"> [Tensorflow2]</a> (by Zhenliang He),
+<a href="https://github.com/luoxier/CycleGAN_Tensorlayer"> [TensorLayer1.0]</a> (by luoxier),
+<a href="https://github.com/tensorlayer/cyclegan"> [TensorLayer2.0]</a> (by zsdonghao),
+<a href="https://github.com/Aixile/chainer-cyclegan">[Chainer]</a> (by Yanghua Jin),
+<a href="https://github.com/yunjey/mnist-svhn-transfer">[Minimal PyTorch]</a> (by yunjey),
+<a href="https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Mxnet-Scala/CycleGAN">[Mxnet]</a> (by Ldpe2G),
+<a href="https://github.com/tjwei/GANotebooks">[lasagne/Keras]</a> (by tjwei),
+<a href="https://github.com/simontomaskarlsson/CycleGAN-Keras">[Keras]</a> (by Simon Karlsson),
+<a href="https://github.com/Ldpe2G/DeepLearningForFun/tree/master/Oneflow-Python/CycleGAN">[OneFlow]</a> (by Ldpe2G)
+</p>
+</ul>
+
+### pix2pix
+<p><a href="https://github.com/affinelayer/pix2pix-tensorflow"> [Tensorflow]</a> (by Christopher Hesse),
+<a href="https://github.com/Eyyub/tensorflow-pix2pix">[Tensorflow]</a> (by Eyyüb Sariu),
+<a href="https://github.com/datitran/face2face-demo"> [Tensorflow (face2face)]</a> (by Dat Tran),
+<a href="https://github.com/awjuliani/Pix2Pix-Film"> [Tensorflow (film)]</a> (by Arthur Juliani),
+<a href="https://github.com/kaonashi-tyc/zi2zi">[Tensorflow (zi2zi)]</a> (by Yuchen Tian),
+<a href="https://github.com/pfnet-research/chainer-pix2pix">[Chainer]</a> (by mattya),
+<a href="https://github.com/tjwei/GANotebooks">[tf/torch/keras/lasagne]</a> (by tjwei),
+<a href="https://github.com/taey16/pix2pixBEGAN.pytorch">[Pytorch]</a> (by taey16)
+</p>
+</ul>
+
+## Prerequisites
+- Linux or macOS
+- Python 3
+- CPU or NVIDIA GPU + CUDA CuDNN
+
+## Getting Started
+### Installation
+
+- Clone this repo:
+```bash
+git clone https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
+cd pytorch-CycleGAN-and-pix2pix
+```
+
+- Install [PyTorch](http://pytorch.org) and 0.4+ and other dependencies (e.g., torchvision, [visdom](https://github.com/facebookresearch/visdom) and [dominate](https://github.com/Knio/dominate)).
+  - For pip users, please type the command `pip install -r requirements.txt`.
+  - For Conda users, you can create a new Conda environment using `conda env create -f environment.yml`.
+  - For Docker users, we provide the pre-built Docker image and Dockerfile. Please refer to our [Docker](docs/docker.md) page.
+  - For Repl users, please click [![Run on Repl.it](https://repl.it/badge/github/junyanz/pytorch-CycleGAN-and-pix2pix)](https://repl.it/github/junyanz/pytorch-CycleGAN-and-pix2pix).
+
+### CycleGAN train/test
+- Download a CycleGAN dataset (e.g. maps):
+```bash
+bash ./datasets/download_cyclegan_dataset.sh maps
+```
+- To view training results and loss plots, run `python -m visdom.server` and click the URL http://localhost:8097.
+- To log training progress and test images to W&B dashboard, set the `--use_wandb` flag with train and test script
+- Train a model:
+```bash
+#!./scripts/train_cyclegan.sh
+python train.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
+```
+To see more intermediate results, check out `./checkpoints/maps_cyclegan/web/index.html`.
+- Test the model:
+```bash
+#!./scripts/test_cyclegan.sh
+python test.py --dataroot ./datasets/maps --name maps_cyclegan --model cycle_gan
+```
+- The test results will be saved to a html file here: `./results/maps_cyclegan/latest_test/index.html`.
+
+### pix2pix train/test
+- Download a pix2pix dataset (e.g.[facades](http://cmp.felk.cvut.cz/~tylecr1/facade/)):
+```bash
+bash ./datasets/download_pix2pix_dataset.sh facades
+```
+- To view training results and loss plots, run `python -m visdom.server` and click the URL http://localhost:8097.
+- To log training progress and test images to W&B dashboard, set the `--use_wandb` flag with train and test script
+- Train a model:
+```bash
+#!./scripts/train_pix2pix.sh
+python train.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
+```
+To see more intermediate results, check out  `./checkpoints/facades_pix2pix/web/index.html`.
+
+- Test the model (`bash ./scripts/test_pix2pix.sh`):
+```bash
+#!./scripts/test_pix2pix.sh
+python test.py --dataroot ./datasets/facades --name facades_pix2pix --model pix2pix --direction BtoA
+```
+- The test results will be saved to a html file here: `./results/facades_pix2pix/test_latest/index.html`. You can find more scripts at `scripts` directory.
+- To train and test pix2pix-based colorization models, please add `--model colorization` and `--dataset_mode colorization`. See our training [tips](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/tips.md#notes-on-colorization) for more details.
+
+### Apply a pre-trained model (CycleGAN)
+- You can download a pretrained model (e.g. horse2zebra) with the following script:
+```bash
+bash ./scripts/download_cyclegan_model.sh horse2zebra
+```
+- The pretrained model is saved at `./checkpoints/{name}_pretrained/latest_net_G.pth`. Check [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/scripts/download_cyclegan_model.sh#L3) for all the available CycleGAN models.
+- To test the model, you also need to download the  horse2zebra dataset:
+```bash
+bash ./datasets/download_cyclegan_dataset.sh horse2zebra
+```
+
+- Then generate the results using
+```bash
+python test.py --dataroot datasets/horse2zebra/testA --name horse2zebra_pretrained --model test --no_dropout
+```
+- The option `--model test` is used for generating results of CycleGAN only for one side. This option will automatically set `--dataset_mode single`, which only loads the images from one set. On the contrary, using `--model cycle_gan` requires loading and generating results in both directions, which is sometimes unnecessary. The results will be saved at `./results/`. Use `--results_dir {directory_path_to_save_result}` to specify the results directory.
+
+- For pix2pix and your own models, you need to explicitly specify `--netG`, `--norm`, `--no_dropout` to match the generator architecture of the trained model. See this [FAQ](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/docs/qa.md#runtimeerror-errors-in-loading-state_dict-812-671461-296) for more details.
+
+### Apply a pre-trained model (pix2pix)
+Download a pre-trained model with `./scripts/download_pix2pix_model.sh`.
+
+- Check [here](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix/blob/master/scripts/download_pix2pix_model.sh#L3) for all the available pix2pix models. For example, if you would like to download label2photo model on the Facades dataset,
+```bash
+bash ./scripts/download_pix2pix_model.sh facades_label2photo
+```
+- Download the pix2pix facades datasets:
+```bash
+bash ./datasets/download_pix2pix_dataset.sh facades
+```
+- Then generate the results using
+```bash
+python test.py --dataroot ./datasets/facades/ --direction BtoA --model pix2pix --name facades_label2photo_pretrained
+```
+- Note that we specified `--direction BtoA` as Facades dataset's A to B direction is photos to labels.
+
+- If you would like to apply a pre-trained model to a collection of input images (rather than image pairs), please use `--model test` option. See `./scripts/test_single.sh` for how to apply a model to Facade label maps (stored in the directory `facades/testB`).
+
+- See a list of currently available models at `./scripts/download_pix2pix_model.sh`
+
+## [Docker](docs/docker.md)
+We provide the pre-built Docker image and Dockerfile that can run this code repo. See [docker](docs/docker.md).
+
+## [Datasets](docs/datasets.md)
+Download pix2pix/CycleGAN datasets and create your own datasets.
+
+## [Training/Test Tips](docs/tips.md)
+Best practice for training and testing your models.
+
+## [Frequently Asked Questions](docs/qa.md)
+Before you post a new question, please first look at the above Q & A and existing GitHub issues.
+
+## Custom Model and Dataset
+If you plan to implement custom models and dataset for your new applications, we provide a dataset [template](data/template_dataset.py) and a model [template](models/template_model.py) as a starting point.
+
+## [Code structure](docs/overview.md)
+To help users better understand and use our code, we briefly overview the functionality and implementation of each package and each module.
+
+## Pull Request
+You are always welcome to contribute to this repository by sending a [pull request](https://help.github.com/articles/about-pull-requests/).
+Please run `flake8 --ignore E501 .` and `python ./scripts/test_before_push.py` before you commit the code. Please also update the code structure [overview](docs/overview.md) accordingly if you add or remove files.
+
+## Citation
+If you use this code for your research, please cite our papers.
+```
+@inproceedings{CycleGAN2017,
+  title={Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks},
+  author={Zhu, Jun-Yan and Park, Taesung and Isola, Phillip and Efros, Alexei A},
+  booktitle={Computer Vision (ICCV), 2017 IEEE International Conference on},
+  year={2017}
+}
+
+
+@inproceedings{isola2017image,
+  title={Image-to-Image Translation with Conditional Adversarial Networks},
+  author={Isola, Phillip and Zhu, Jun-Yan and Zhou, Tinghui and Efros, Alexei A},
+  booktitle={Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on},
+  year={2017}
+}
+```
+
+## Other Languages
+[Spanish](docs/README_es.md)
+
+## Related Projects
+**[contrastive-unpaired-translation](https://github.com/taesungp/contrastive-unpaired-translation) (CUT)**<br>
+**[CycleGAN-Torch](https://github.com/junyanz/CycleGAN) |
+[pix2pix-Torch](https://github.com/phillipi/pix2pix) | [pix2pixHD](https://github.com/NVIDIA/pix2pixHD)|
+[BicycleGAN](https://github.com/junyanz/BicycleGAN) | [vid2vid](https://tcwang0509.github.io/vid2vid/) | [SPADE/GauGAN](https://github.com/NVlabs/SPADE)**<br>
+**[iGAN](https://github.com/junyanz/iGAN) | [GAN Dissection](https://github.com/CSAILVision/GANDissect) | [GAN Paint](http://ganpaint.io/)**
+
+## Cat Paper Collection
+If you love cats, and love reading cool graphics, vision, and learning papers, please check out the Cat Paper [Collection](https://github.com/junyanz/CatPapers).
+
+## Acknowledgments
+Our code is inspired by [pytorch-DCGAN](https://github.com/pytorch/examples/tree/master/dcgan).

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checkpoints/bw2color/loss_log.txt

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+================ Training Loss (Sat Nov  4 23:03:46 2023) ================
+(epoch: 8, iters: 2, time: 0.198, data: 0.717) G_GAN: 1.010 G_L1: 7.272 D_real: 0.533 D_fake: 0.516 
+(epoch: 15, iters: 4, time: 0.199, data: 0.001) G_GAN: 1.228 G_L1: 4.958 D_real: 0.364 D_fake: 0.573 
+(epoch: 22, iters: 6, time: 0.227, data: 0.003) G_GAN: 1.043 G_L1: 4.291 D_real: 0.214 D_fake: 0.797 
+(epoch: 29, iters: 8, time: 2.188, data: 0.001) G_GAN: 0.901 G_L1: 2.646 D_real: 0.669 D_fake: 0.546 
+(epoch: 36, iters: 10, time: 0.257, data: 0.005) G_GAN: 1.026 G_L1: 2.751 D_real: 0.526 D_fake: 0.560 
+(epoch: 43, iters: 12, time: 0.242, data: 0.001) G_GAN: 1.380 G_L1: 3.614 D_real: 0.305 D_fake: 0.586 
+(epoch: 50, iters: 14, time: 0.256, data: 0.003) G_GAN: 0.709 G_L1: 2.387 D_real: 0.763 D_fake: 0.772 
+(epoch: 58, iters: 2, time: 2.606, data: 0.526) G_GAN: 0.973 G_L1: 3.211 D_real: 0.583 D_fake: 0.805 
+(epoch: 65, iters: 4, time: 0.239, data: 0.005) G_GAN: 0.849 G_L1: 2.521 D_real: 0.685 D_fake: 0.521 
+(epoch: 72, iters: 6, time: 0.227, data: 0.004) G_GAN: 0.768 G_L1: 2.132 D_real: 0.898 D_fake: 0.606 
+(epoch: 79, iters: 8, time: 0.186, data: 0.003) G_GAN: 0.764 G_L1: 1.370 D_real: 0.824 D_fake: 0.625 
+(epoch: 86, iters: 10, time: 1.048, data: 0.020) G_GAN: 1.167 G_L1: 3.618 D_real: 0.286 D_fake: 0.943 
+(epoch: 93, iters: 12, time: 0.256, data: 0.001) G_GAN: 0.800 G_L1: 1.420 D_real: 0.879 D_fake: 0.532 
+(epoch: 100, iters: 14, time: 0.250, data: 0.003) G_GAN: 0.689 G_L1: 1.218 D_real: 0.590 D_fake: 0.869 
+(epoch: 108, iters: 2, time: 0.168, data: 0.382) G_GAN: 0.871 G_L1: 2.465 D_real: 0.585 D_fake: 0.526 
+(epoch: 115, iters: 4, time: 1.077, data: 0.006) G_GAN: 0.732 G_L1: 1.168 D_real: 0.869 D_fake: 0.569 

checkpoints/bw2color/opt.txt

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+------------ Options -------------
+align_data: False
+aspect_ratio: 1.0
+batchSize: 1
+checkpoints_dir: ./checkpoints
+dataroot: None
+display_id: 1
+display_winsize: 256
+fineSize: 256
+gpu_ids: []
+how_many: 50
+identity: 0.0
+image_path: C:\Users\thera\Downloads\DataSet\09.png
+input_nc: 3
+isTrain: False
+loadSize: 286
+max_dataset_size: inf
+model: colorization
+nThreads: 2
+n_layers_D: 3
+name: bw2color
+ndf: 64
+ngf: 64
+norm: instance
+ntest: inf
+output_nc: 3
+phase: test
+results_dir: ./results/
+serial_batches: False
+use_dropout: True
+which_direction: AtoB
+which_epoch: latest
+which_model_netD: basic
+which_model_netG: resnet_9blocks
+-------------- End ----------------

checkpoints/bw2color/test_opt.txt

@@ -0,0 +1,45 @@
+----------------- Options ---------------
+             aspect_ratio: 1.0                           
+               batch_size: 1                             
+          checkpoints_dir: ./checkpoints                 
+                crop_size: None                          
+                 dataroot: None                          
+             dataset_mode: colorization                  
+                direction: AtoB                          
+          display_winsize: 256                           
+                    epoch: latest                        
+                     eval: False                         
+                  gpu_ids: -1                            	[default: 0]
+                 how_many: 50                            
+               image_path: C:\Users\thera\Downloads\55Sin t�tulo.png	[default: None]
+                init_gain: 0.02                          
+                init_type: normal                        
+                 input_nc: 1                             
+                  isTrain: False                         	[default: None]
+                load_iter: 0                             	[default: 0]
+                load_size: None                          
+         max_dataset_size: inf                           
+                    model: colorization                  
+               n_layers_D: 3                             
+                     name: bw2color                      	[default: experiment_name]
+                      ndf: 64                            
+                     netD: basic                         
+                     netG: unet_256                      
+                      ngf: 64                            
+               no_dropout: False                         
+                  no_flip: False                         
+                     norm: batch                         
+                    ntest: inf                           
+                 num_test: 50                            
+              num_threads: 4                             
+                output_nc: 2                             
+                    phase: test                          
+               preprocess: resize_and_crop               
+              results_dir: ./results/                    
+           serial_batches: False                         
+                   suffix:                               
+                use_wandb: False                         
+                  verbose: False                         
+       wandb_project_name: CycleGAN-and-pix2pix          
+              which_epoch: latest                        
+----------------- End -------------------